Abstract
In this paper we discuss the performance of classical classification methods on Big Data. We distinguish the cases many features and many observations. For the many features case we look at projection methods, distance-based methods, and feature selection. For the many observations case we mainly consider subsampling. The examples in this paper show that standard classification methods should not be blindly applied to Big Data.
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Notes
- 1.
Thanks to T. Glasmachers for suggesting this definition.
- 2.
This part of the paper was supported by the Mercator Research Center Ruhr, grant Pr-2013-0015, see http://www.largescalesvm.de/.
- 3.
This simulation was carried out using the R-packages BatchJobs (Bischl et al. 2015) and mlr on the SLURM cluster of the Statistics Department of TU Dortmund University.
- 4.
This example is inspired by Fan et al. (2011).
- 5.
We used the R-library libSVM, see http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
- 6.
Data sets taken from http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/.
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Weihs, C., Horn, D., Bischl, B. (2016). Big Data Classification : Aspects on Many Features and Many Observations. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_10
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DOI: https://doi.org/10.1007/978-3-319-25226-1_10
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